Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs.

Authors

Mohammad Sholeh, Masoumeh Beig, Negin Kiani, Farzad Badmasti

Published in

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases. Pages 105998. Jul 24, 2026. Epub Jul 24, 2026.

Abstract

Escherichia coli poses a global health threat from increasing β-lactam resistance. This study uses genomic and One-Health data to map resistance patterns and enhance antimicrobial resistance (AMR) prediction and management.
This study performed a One-Health whole-genome analysis of 30,554 E. coli isolates from human, animal, and environmental sources, spanning from 2000 to 2025. Publicly available genomic data were retrieved from NCBI, encompassing β-lactam resistance genes, including extended-spectrum β-lactamases, AmpC, and carbapenemases, along with key chromosomal resistance modifiers. These genes were identified and curated using AMRFinderPlus. Multilocus sequence typing (MLST) and pathotype assignments were conducted in silico. Temporal, host, sequence-type (ST), and geographic patterns of resistance were modeled, with gradient-boosted machine learning algorithms predicting minimum inhibitory concentrations (MICs) based on antibiotic resistance gene profiles and chromosomal features. Temporal and geographic resistance patterns were further analyzed using statistical and visualization tools in R and Python.
A total of 30,554 E. coli whole-genome sequences from isolates spanning 126 countries between 2000 and 2025 were analyzed. The dataset included 2015 complete genomes, 177 chromosome-level assemblies, 8310 scaffold-level assemblies, and 20,052 contig-level assemblies, with an average sequence length of 5,120,983 bp. Isolates were categorized by host source: 14,320 from humans, 8184 from animals, 3941 from environmental sources, and 4109 with an unknown source. MLST was successfully performed on 29,648 isolates, identifying dominant STs such as ST131, ST11, and ST10. Human isolates were predominantly associated with epidemic clones ST131, ST73, and ST1193, while animal isolates were associated with ST10, and environmental isolates showed a strong presence of ST155. Temporal analyses indicated a steady increase in β-lactam resistance, with bla_CTX-M-15, bla_NDM-5, and bla_OXA-1 showing the most significant prevalence trends. Model performance was strongest for carbapenems, particularly imipenem (R² = 0.88) and ertapenem (R² = 0.74), whereas predictions for ampicillin and piperacillin-tazobactam showed poor agreement with observed MICs.
Global analysis of 30,554 genomes shows rising β-lactam resistance driven by key genes (bla_CTX-M-15, bla_NDM-5, bla_OXA-1) in high-risk clones. Machine learning accurately predicted carbapenem resistance but struggled with β-lactamase inhibitor combos. Integrating genomics and One-Health data can guide AMR surveillance and control.

PMID:
42498094
Bibliographic data and abstract were imported from PubMed on 25 Jul 2026.

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